Noise reduction and mask removal neural network for X-ray single-particle imaging
© Alfredo Bellisario et al. 2022.
| Veröffentlicht in: | Journal of applied crystallography. - 1998. - 55(2022), Pt 1 vom: 01. Feb., Seite 122-132 |
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| Weitere Verfasser: | , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | Journal of applied crystallography |
| Schlagworte: | Journal Article XFELs coherent X-ray diffractive imaging (CXDI) diffract-then-destroy free-electron lasers imaging protein structures single particles |
| Zusammenfassung: | © Alfredo Bellisario et al. 2022. Free-electron lasers could enable X-ray imaging of single biological macromolecules and the study of protein dynamics, paving the way for a powerful new imaging tool in structural biology, but a low signal-to-noise ratio and missing regions in the detectors, colloquially termed 'masks', affect data collection and hamper real-time evaluation of experimental data. In this article, the challenges posed by noise and masks are tackled by introducing a neural network pipeline that aims to restore diffraction intensities. For training and testing of the model, a data set of diffraction patterns was simulated from 10 900 different proteins with molecular weights within the range of 10-100 kDa and collected at a photon energy of 8 keV. The method is compared with a simple low-pass filtering algorithm based on autocorrelation constraints. The results show an improvement in the mean-squared error of roughly two orders of magnitude in the presence of masks compared with the noisy data. The algorithm was also tested at increasing mask width, leading to the conclusion that demasking can achieve good results when the mask is smaller than half of the central speckle of the pattern. The results highlight the competitiveness of this model for data processing and the feasibility of restoring diffraction intensities from unknown structures in real time using deep learning methods. Finally, an example is shown of this preprocessing making orientation recovery more reliable, especially for data sets containing very few patterns, using the expansion-maximization-compression algorithm |
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| Beschreibung: | Date Revised 05.11.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
| ISSN: | 0021-8898 |
| DOI: | 10.1107/S1600576721012371 |